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***This role is 3 days onsite each week in Chicago***
***Position is bonus eligible***
Prestigious Financial Institution is currently seeking a Principal AI-Native SDLC Architect. Candidate will design a graduated SDLC framework with prescriptive practice standards at each tier of criticality, pilot with real development teams, and drive adoption across the enterprise. The deliverable is a working development lifecycle — not a document.
This is a rare opportunity to define how a critical financial market utility builds software in the AI era. You will have executive sponsorship, organizational mandate, and the backing to make standards enforceable rather than advisory.
Responsibilities:
This is a senior individual contributor role that leads through influence and organizational authority, not direct management. You will build consensus across multiple functions while being opinionated enough to define standards with teeth. You will have executive sponsorship from the CSO and CIO to make standards enforceable.
Design Graduated SDLC Standards and Practices
Define prescriptive practice standards across multiple tiers of development criticality — from personal experimentation through operationally critical, SCI-adjacent systems
Establish standards across key dimensions: structure, testing, documentation, error handling, dependencies, storage, review, and operability
Elevate existing documentation, architecture decisions, integration patterns, and deployment procedures to the level of detail that drives consistent results from both human and AI developers
Design documentation and testing standards that serve double duty: readable by human developers joining a project and consumable by AI agents starting a coding session
Encode tier-appropriate standards into persistent context files (CLAUDE.md, AGENTS.md, planning templates) as a natural output of well-documented practices — not as a separate AI-specific exercise
Design graduation triggers and assessment mechanisms for artifacts moving between tiers
Enable Citizen Development at Scale
Create low-friction pathways for lower-tier development that enable experimentation without bureaucratic overhead
Design intake, classification, and registry processes for citizen-developed tools — visibility without paralysis
Solve the path from working prototype to supported environment at every tier
Drive Organizational Adoption
Pilot the graduated SDLC with 2 development teams, iterating based on real-world feedback
Build alignment across Engineering, Architecture, Security, QA, and Governance/Risk functions
Establish a quarterly organizational learning system where operational data, code review patterns, graduation assessments, and incident post-mortems feed back into living standards
Develop training and coaching programs that help developers at every level internalize the standards — not just comply with them
Connect to Enterprise Architecture and Governance
Provide the practice standards layer underneath the existing governance framework
Ensure standards satisfy regulatory requirements (Regulation SCI, CPMI-IOSCO, internal audit)
Coordinate with enterprise architecture to ensure SDLC standards reflect infrastructure reality
Qualifications:
10 years in software engineering, architecture, or development methodology roles
Demonstrated experience designing and implementing SDLC standards or development practice frameworks at organizational scale — not single-team
Hands-on experience with AI coding agents (Claude Code, GitHub Copilot, Cursor, or equivalent) in real codebases — not just demos
Working knowledge of context engineering: persistent context files, research-plan-implement workflows, progressive disclosure, context window management
Experience with brownfield/legacy codebases — understanding why AI-assisted development in complex existing systems is fundamentally different from greenfield
Strong facilitation and coalition-building skills — ability to drive alignment across engineering, security, architecture, and governance functions
Excellent written and verbal communication — ability to translate technical concepts for executives and organizational standards for AI agents
Preferred Skills
Experience in financial services, particularly regulated environments (SIFMU, bank, exchange, clearinghouse)
Familiarity with NIST AI Risk Management Framework, Regulation SCI, or CPMI-IOSCO principles
Experience with CI/CD pipelines, automated testing frameworks, and quality gate enforcement
Background in organizational change management — understanding that SDLC transformation is a culture change, not a standards document
Track record of staying current with rapidly evolving AI development practices — demonstrated through conference participation, publication, or community engagement
***This role is 3 days onsite each week in Chicago***
***Position is bonus eligible***
Prestigious Financial Institution is currently seeking a Principal AI-Native SDLC Architect. Candidate will design a graduated SDLC framework with prescriptive practice standards at each tier of criticality, pilot with real development teams, and drive adoption across the enterprise. The deliverable is a working development lifecycle — not a document.
This is a rare opportunity to define how a critical financial market utility builds software in the AI era. You will have executive sponsorship, organizational mandate, and the backing to make standards enforceable rather than advisory.
Responsibilities:
This is a senior individual contributor role that leads through influence and organizational authority, not direct management. You will build consensus across multiple functions while being opinionated enough to define standards with teeth. You will have executive sponsorship from the CSO and CIO to make standards enforceable.
Design Graduated SDLC Standards and Practices
Define prescriptive practice standards across multiple tiers of development criticality — from personal experimentation through operationally critical, SCI-adjacent systems
Establish standards across key dimensions: structure, testing, documentation, error handling, dependencies, storage, review, and operability
Elevate existing documentation, architecture decisions, integration patterns, and deployment procedures to the level of detail that drives consistent results from both human and AI developers
Design documentation and testing standards that serve double duty: readable by human developers joining a project and consumable by AI agents starting a coding session
Encode tier-appropriate standards into persistent context files (CLAUDE.md, AGENTS.md, planning templates) as a natural output of well-documented practices — not as a separate AI-specific exercise
Design graduation triggers and assessment mechanisms for artifacts moving between tiers
Enable Citizen Development at Scale
Create low-friction pathways for lower-tier development that enable experimentation without bureaucratic overhead
Design intake, classification, and registry processes for citizen-developed tools — visibility without paralysis
Solve the path from working prototype to supported environment at every tier
Drive Organizational Adoption
Pilot the graduated SDLC with 2 development teams, iterating based on real-world feedback
Build alignment across Engineering, Architecture, Security, QA, and Governance/Risk functions
Establish a quarterly organizational learning system where operational data, code review patterns, graduation assessments, and incident post-mortems feed back into living standards
Develop training and coaching programs that help developers at every level internalize the standards — not just comply with them
Connect to Enterprise Architecture and Governance
Provide the practice standards layer underneath the existing governance framework
Ensure standards satisfy regulatory requirements (Regulation SCI, CPMI-IOSCO, internal audit)
Coordinate with enterprise architecture to ensure SDLC standards reflect infrastructure reality
Qualifications:
10 years in software engineering, architecture, or development methodology roles
Demonstrated experience designing and implementing SDLC standards or development practice frameworks at organizational scale — not single-team
Hands-on experience with AI coding agents (Claude Code, GitHub Copilot, Cursor, or equivalent) in real codebases — not just demos
Working knowledge of context engineering: persistent context files, research-plan-implement workflows, progressive disclosure, context window management
Experience with brownfield/legacy codebases — understanding why AI-assisted development in complex existing systems is fundamentally different from greenfield
Strong facilitation and coalition-building skills — ability to drive alignment across engineering, security, architecture, and governance functions
Excellent written and verbal communication — ability to translate technical concepts for executives and organizational standards for AI agents
Preferred Skills
Experience in financial services, particularly regulated environments (SIFMU, bank, exchange, clearinghouse)
Familiarity with NIST AI Risk Management Framework, Regulation SCI, or CPMI-IOSCO principles
Experience with CI/CD pipelines, automated testing frameworks, and quality gate enforcement
Background in organizational change management — understanding that SDLC transformation is a culture change, not a standards document
Track record of staying current with rapidly evolving AI development practices — demonstrated through conference participation, publication, or community engagement